Voice Deepfakes Emerge as Critical Enterprise Security Threat
Reality Defender warns enterprises about escalating voice deepfake attacks targeting corporate communications, highlighting the urgent need for real-time audio authentication systems.
Reality Defender warns enterprises about escalating voice deepfake attacks targeting corporate communications, highlighting the urgent need for real-time audio authentication systems.
New research proposes treating AI models as clinical patients, introducing systematic diagnostic and treatment protocols for understanding model behavior, identifying failures, and applying targeted interventions.
New research exposes a critical flaw in AI safety systems: models tasked with monitoring AI outputs show systematic bias when evaluating content they generated themselves.
New research investigates representation collapse in continual learning, revealing why neural networks catastrophically forget previous tasks and proposing mechanisms to understand this fundamental limitation.
New research introduces SkillNet, a framework for creating, evaluating, and connecting modular AI skills that can be composed into complex agent capabilities.
New research introduces quantized KV cache persistence for running multi-agent LLM systems on resource-constrained edge hardware, enabling local AI agents without cloud dependency.
Biometric verification provider iProov now processes over one million identity checks daily as organizations scramble to counter sophisticated deepfake-powered fraud attacks.
Four emerging protocols—MCP, A2A, ACP, and ANP—are defining how AI agents communicate, share context, and collaborate. Here's what each does and why it matters.
Netflix has acquired InterPositive, Ben Affleck's AI filmmaking company, signaling a major shift in how streaming giants approach synthetic media and AI-assisted content production.
New research reveals AI agents can identify anonymous accounts by analyzing writing patterns, behavioral data, and cross-platform activity, raising major privacy and authenticity concerns.
Researchers introduce an automated framework for discovering the hidden concepts LLM evaluators use when judging AI outputs, enabling better understanding and improvement of AI content assessment systems.
New research explores semantic caching strategies for LLM embeddings, moving beyond exact-match lookups to approximate retrieval methods that could dramatically reduce computational costs.